pith:MAZUAETN
Beyond LLMs, Sparse Distributed Memory, and Neuromorphics <A Hyper-Dimensional SRAM-CAM "VaCoAl" for Ultra-High Speed, Ultra-Low Power, and Low Cost>
Inverting Galois-field algebra in hyperdimensional computing creates a predictable STDP-equivalent selection mechanism.
arxiv:2604.11665 v5 · 2026-04-13 · cs.NE · cs.AI
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\pithnumber{MAZUAETNUUXBBQKDMWZXO5RN3G}
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Claims
In a deterministic HDC architecture that inverts the conventional role of Galois-field algebra, a path-dependent semantic selection mechanism emerges equivalent to STDP, with magnitude predictable a priori from a closed-form expression matching measured values; VaCoAl defines a third paradigm HDC-AI with a phase transition and Kuhnian paradigm shift.
That the inversion of Galois-field algebra for relative similarity ranking produces an STDP-equivalent mechanism whose magnitude is independently predictable from a closed-form expression rather than being a direct consequence of the ranking definition itself, and that this architecture structurally solves catastrophic forgetting and the binding problem at an algebraic level.
VaCoAl is a hyperdimensional algorithm using inverted Galois-field operations on high-dimensional binary spaces to enable path-dependent semantic selection equivalent to STDP, with a claimed closed-form magnitude, demonstrated on large Wikidata relation graphs.
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| First computed | 2026-05-22T02:04:40.906349Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
603340126da52e10c14365b377762dd9aa896f15fbb0ce067a69435777869ca6
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/MAZUAETNUUXBBQKDMWZXO5RN3G \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 603340126da52e10c14365b377762dd9aa896f15fbb0ce067a69435777869ca6
Canonical record JSON
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